scholarly journals Colored facial image restoration by similarity enhanced implicative fuzzy association memory

2019 ◽  
Vol 13 (1) ◽  
pp. 199 ◽  
Author(s):  
Kwang Baek Kim ◽  
Doo Heon Song
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Associative Memory ◽  
Memory Model ◽  
Mean Square ◽  
Facial Image ◽  
Digital Forensic ◽  
Association Memory ◽  
Fuzzy Associative Memory ◽  
Noise Tolerant

Image restoration refers to the recovery of an underlying image from an observation that has been corrupted by various types of noise. In a digital forensic software, such image restoration process should be noise-tolerant, robust, fast, and scalable.  In this paper, we apply implicative fuzzy association memory structure in colored facial image restoration with enhanced similarity measure involved in output computarion. The efficacy if the proposed fuzzy associative memory model is verified by the experiment in that it was 95% successful (with zero mean square error) out of 20 tested images.

scholarly journals Self-Similarity Sparse Representation for Image Restoration

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1063-1067
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Gaussian Noise ◽  
Local Similarity ◽  
Mean Square ◽  
Regularization Term ◽  
Local Regularization ◽  
Degraded Image ◽  
Local Patterns ◽  
Non Local

Image restoration aims to restore an image from a degraded image. The degradation may occur during image acquisition or image transmission. Image degradation lowers the quality of the image. In this paper additive Gaussian noise is considered for degrading the original image. For restoring the image from degraded image the proposed method used both local and non-local similarity patterns. The restoration problem is modeled with regression model. Two regularization terms are considered for representing prior image information. One regularization term is for local patterns and other is for non-local similarity patterns. The additive local regularization term is used to restore the edges. The non-local regularization term works best for local smoothness and edge information will be lost. The proposed algorithm took a clean image of size 256x256 and added with Gaussian noise with different levels of noise levels. A self-adaptive dictionary is trained for a particular window of image with local and non-local patterns and stacked to three dimensional matrix. The patch size considered for training the dictionary is 10x10. For restoring each patch it searches best atoms form the trained dictionary. The efficiency of the algorithm is estimated by parameters mean square error, root mean square error, PSNR and FSIM. The algorithm is also tested for different images like cameraman, house, Barbara, Lena and parrot. The proposed algorithm is tested with conventional algorithms. .

scholarly journals Performance Evaluation and Comparison of Different Noise, Apply on PNG Image Format Used in Deconvolution Wiener Filter (FFT) Algorithm

2015 ◽  
Vol 4 ◽  
pp. 8-14 ◽  
Author(s):  
Kalpana Chaurasia ◽  
Nidhi Sharma
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Absolute Error ◽  
Mean Square ◽  
Degraded Image ◽  
Blurred Image ◽  
Degraded Images

Image Restoration is a field of Image Processing. This deals with recovering an original and sharp image from a degraded image using degradation & restoration function. This study focus on restoration of degraded images which have been blurred by known degradation function. PNG (Tag Index Format) are considered for analyzing the image restoration techniques deconvolution using wiener filter (FFT) algorithm with an information of the Point Spread Function (PSF) corrupted blurred image and then corrupted by Different noise. Performance analysis is done to measure the efficiency by which image is recovered. The analysis is done on the basis of various performance metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error(MSE),Root Mean Square Error (RMSE), Mean Absolute Error (MAE).

Iterative linear minimum mean-square-error image restoration from partially known blur

2000 ◽  
Vol 17 (4) ◽  
pp. 711 ◽  
Author(s):  
Vladimir Z. Mesarović ◽  
Nikolas P. Galatsanos ◽  
Miles N. Wernick
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Mean Square

scholarly journals Image Restoration by Linear Regression for Gaussian Noise Removal from Natural Images

2019 ◽  
Vol 8 (11S2) ◽  
pp. 126-130
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Gaussian Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Noise Removal ◽  
Support Vector ◽  
Mean Square ◽  
Degraded Image ◽  
Index Measure

Image restoration improves the features information of degraded or corrupted image. The degradation of image because of addition of noise when acquiring the image. Many algorithms are developed by many researches. In this paper image is corrupted by Gaussian noise to generate degraded image. The image is restored from this degraded image by supervised learning based algorithm. Few images are considered for training the dictionary with each element of size 9x9. The degraded image is considered patch by patch for restoring the patch from the trained set of images by support vector machine. The quality assessment of the image done by comparing the quality matrices like mean square error, root mean square error, peak signal to noise ratio, structural similarity index measure and feature similarity index measure. In this paper the images are considered are cameraman, house, Lena, Barbara and Parrot

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

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